SoroushVahidi/scidocs huggingface dataset:
Mars Sample Return campaign status
(2012)

The proposed Mars Sample Return (MSR) Campaign would be the next big step in the Mars Exploration Program (MEP). Identified by the Planetary Decadal Survey of 2011 as the highest science priority for large missions, the Mars Program has been working in partnership with the European Space Agency (ESA) to define a campaign to return a surface sample of martian surface material to Earth for analysis. As currently envisioned, the MSR campaign concept consists of a series of flight missions to collect and return the sample, and a ground based project to develop a sample receiving facility. The first mission in this proposed campaign would be baselined for launch in 2018, and would consist of a rover capable of scientifically selecting and caching the sample for retrieval by a later mission. Subsequent missions would retrieve the cache, launch it into orbit around Mars, where it would be captured by an orbiter and returned to the Earth. This paper discusses the current status of the MSR Campaign architecture definition and requirements development and near term plans for technical definition and review. Also discussed are selected design drivers and proposed key requirements which would have to be addressed in the definition of the campaign.1, 2
Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations
(2016)

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.
On-line cursive script recognition using time-delay neural networks and hidden Markov models
(1994)

We present a writer-independent system for online handwriting recognition that can handle a variety of writing styles including cursive script and handprinting. The input to our system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. A time-delay neural network is used to estimate a posteriori probabilities for characters in a word. A hidden Markov model segments the word in a way that optimizes the global word score, using a dictionary in the process. A geometrical normalization scheme and a fast but efficient dictionary search are also presented. Trained on 20 k words from 59 writers, using a 25 k-word dictionary, our system reached recognition rates of 89% for characters and 80% for words on test data from a disjoint set of writers.
Force of Habit and Information Systems Usage: Theory and Initial Validation
(2003)

Over the last two decades, information systems (IS) research has primarily focused on people’s conscious (intentional) behavior when trying to explain and predict IS usage. Consequently, almost no research has investigated the potential importance of subconscious (automatic) behaviors, also known as habits. This study represents a first step toward validating the idea that one can add explanatory power to a behavioral model such as Ajzen’s [1985] theory of planned behavior (TPB) by including the habit construct. We conducted a two-stage questionnaire-based survey involving two different groups of students who had access to a sophisticated internet-based communication tool (IBCT). These data were used to test a behavioral model integrating theoretical constructs of TPB and a relevant subset of Triandis’ [1980] behavioral framework. Our findings highlight the importance of considering both conscious (intentions) and subconscious (habits) factors in explaining usage behavior. Furthermore, we share our observations about antecedents of IBCT usage in the educational context. Implications for practice and research are discussed.
Interacting with in-vehicle systems: understanding, measuring, and evaluating attention
(2009)

In-vehicle systems research is becoming a significant field as the market for in-vehicle systems continue to grow. As a consequence, researchers are increasingly concerned with opportunities and limitations of HCI in a moving vehicle. Especially aspects of attention constitute a challenge for in-vehicle systems development. This paper seeks to remedy this by defining and exemplifying attention understandings. 100 papers were classified in a two-fold perspective; under what settings are in-vehicle systems evaluated and how is driver attention measured in regard to in-vehicle systems HCI. A breakdown of the distribution of driving settings and measures is presented and the impact of driver attention is discussed. The classification revealed that most of the studies were conducted in driving simulators and real traffic driving, while lateral and longitudinal control and eye behaviour were the most used measures. Author
Chemi-net: a graph convolutional network for accurate drug property prediction
(2018)

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.
Threatening faces and social anxiety: a literature review.
(2010)

A threatening facial expression is a potent social sign of hostility or dominance. During the past 20 years, photographs of threatening faces have been increasingly included as stimuli in studies with socially anxious participants, based on the hypothesis that a threatening face is especially salient to people with fears of social interaction or negative evaluation. The purpose of this literature review is to systematically evaluate the accumulated research and suggest possible avenues for further research. The main conclusion is that photographs of threatening faces engage a broad range of perceptual processes in socially anxious participants, particularly when exposure times are very short.
A 1 . 5V , 10-bit , 14 . 3-MS / s CMOS Pipeline Analog-to-Digital Converter
(1999)

A 1.5-V, 10-bit, 14.3-MS/s pipeline analog-to-digital converter was implemented in a 0.6m CMOS technology. Emphasis was placed on observing device reliability constraints at low voltage. MOS switches were implemented without lowthreshold devices by using a bootstrapping technique that does not subject the devices to large terminal voltages. The converter achieved a peak signal-to-noise-and-distortion ratio of 58.5 dB, maximum differential nonlinearity of 0.5 least significant bit (LSB), maximum integral nonlinearity of 0.7 LSB, and a power consumption of 36 mW.
Remote Sensing Image Classification Based on Stacked Denoising Autoencoder
(2018)

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP) neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1) remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.
A wearable fingertip haptic device with 3 DoF asymmetric 3-RSR kinematics
(2015)

A novel wearable haptic device for modulating skin stretch at the fingertip is presented. Rendering of skin stretch in 3 degrees of freedom (DoF), with contact - no contact capabilities, was implemented through rigid parallel kinematics. The novel asymmetrical three revolute-spherical-revolute (3-RSR) configuration allowed compact dimensions with minimum encumbrance of the hand workspace and minimum inter-finger interference. A differential method for solving the non-trivial inverse kinematics is proposed and implemented in real time for controlling the position of the skin tactor. Experiments involving the grasping of a virtual object were conducted using two devices (thumb and index fingers) in a group of 4 subjects: results showed that participants performed the grasping task more precisely and with grasping forces closer to the expected natural behavior when the proposed device provided haptic feedback.
Deformable Classifiers
(2017)

Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transformations. In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, and a transformation of the object image to a reference instantiation is computed in terms of the classifier output, separately for each class. The classifier outputs for each class, after transformation, are compared to yield the final decision. As a by-product of the classification this yields a transformation of the input object to a reference pose, which can be used for downstream tasks such as the computation of object support. We apply a two-step training mechanism for our framework, which alternates between optimizing over the latent transformation variables and the classifier parameters to minimize the loss function. We show that multilayer perceptrons, also known as deep networks, are well suited for this approach and achieve state of the art results on the rotated MNIST and the Google Earth dataset, and produce competitive results on MNIST and CIFAR-10 when training on smaller subsets of training data.
Evolutionary Delivery versus the "waterfall model"
(1985)

The conventional wisdom of planning software engineering projects, using the widely cited "waterfall model" is not the only useful software development process model. In fact, the "waterfall model" may be unrealistic, and dangerous to the primary objectives of any software project.The alternative model, which I choose to call "evolutionary delivery" is not widely taught or practiced yet. But there is already more than a decade of practical experience in using it. In various forms. It is quite clear from these experiences that evolutionary delivery is a powerful general tool for both software development and associated systems development.Almost all experienced software developers do make use of some of the ideas in evolutionary development at one time or another. But, this is often unplanned, informal and it is an incomplete exploitation of this powerful method. This paper will try to expose the theoretical and practical aspects of the method in a fuller perspective. We need to learn the theory fully, so that we can apply and learn it completely.
Deep Generative Models with Learnable Knowledge Constraints
(2018)

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified a priori, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. The resulting algorithm is modelagnostic to apply to any DGMs, and is flexible to adapt arbitrary constraints with the model jointly. Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models.
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
(2016)

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e., deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision – convolutional neural networks (CNNs). We start with “AlexNet” as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.
Glaucoma-Deep : Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning Detection of Glaucoma by Abbas
(2017)

Detection of glaucoma eye disease is still a challenging task for computer-aided diagnostics (CADx) systems. During eye screening process, the ophthalmologists measures the glaucoma by structure changes in optic disc (OD), loss of nerve fibres (LNF) and atrophy of the peripapillary region (APR). In retinal images, the automated CADx systems are developed to assess this eye disease through segmentation-based hand-crafted features. Therefore in this paper, the convolutional neural network (CNN) unsupervised architecture was used to extract the features through multilayer from raw pixel intensities. Afterwards, the deep-belief network (DBN) model was used to select the most discriminative deep features based on the annotated training dataset. At last, the final decision is performed by softmax linear classifier to differentiate between glaucoma and non-glaucoma retinal fundus image. This proposed system is known as Glaucoma-Deep and tested on 1200 retinal images obtained from publically and privately available datasets. To evaluate the performance of Glaucoma-Deep system, the sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRC) statistical measures were utilized. On average, the SE of 84.50%, SP of 98.01%, ACC of 99% and PRC of 84% values were achieved. Comparing to state-of-the-art systems, the Nodular-Deep system accomplished significant higher results. Consequently, the Glaucoma-Deep system can easily recognize the glaucoma eye disease to solve the problem of clinical experts during eye-screening process on large-scale environments. Keywords—Fundus imaging; glaucoma; diabetic retinopathy; deep learning; convolutional neural networks; deep belief network
On Laser Ranging Based on High-Speed/Energy Laser Diode Pulses and Single-Photon Detection Techniques
(2015)

This paper discusses the construction principles and performance of a pulsed time-of-flight (TOF) laser radar based on high-speed (FWHM ~100 ps) and high-energy (~1 nJ) optical transmitter pulses produced with a specific laser diode working in an “enhanced gain-switching” regime and based on single-photon detection in the receiver. It is shown by analysis and experiments that single-shot precision at the level of 2W3 cm is achievable. The effective measurement rate can exceed 10 kHz to a noncooperative target (20% reflectivity) at a distance of > 50 m, with an effective receiver aperture size of 2.5 cm2. The effect of background illumination is analyzed. It is shown that the gating of the SPAD detector is an effective means to avoid the blocking of the receiver in a high-level background illumination case. A brief comparison with pulsed TOF laser radars employing linear detection techniques is also made.
Detecting Structural Similarities between XML Documents
(2002)

In this paper we propose a technique for detecting the similarity in the structure of XML documents. The technique is based on the idea of representing the structure of an XML document as a time series in which each occurrence of a tag corresponds to a given impulse. By analyzing the frequencies of the corresponding Fourier transform, we can hence state the degree of similarity between documents. The efficiency and effectiveness of this approach are compelling when compared with traditional ones.
Translating Learning into Numbers: A Generic Framework for Learning Analytics
(2012)

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.
Trust-aware crowdsourcing with domain knowledge
(2015)

The rise of social network and crowdsourcing platforms makes it convenient to take advantage of the collective intelligence to estimate true labels of questions of interest. However, input from workers is often noisy and even malicious. Trust is used to model workers in order to better estimate true labels of questions. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. Finally, we demonstrate that our model is superior to state-of-the-art by testing it on multiple real-world datasets.
GORAM – Group ORAM for Privacy and Access Control in Outsourced Personal Records 1
(2015)

Cloud storage has rapidly become a cornerstone of many IT infrastructures, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. Putting user data in the direct control of cloud service providers, however, raises security and privacy concerns related to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. Furthermore, even if the cloud provider is trusted, users having access to outsourced files might be malicious and misbehave. These concerns are particularly serious in sensitive applications like personal health records and credit score systems. To tackle this problem, we present GORAM, a cryptographic system that protects the secrecy and integrity of outsourced data with respect to both an untrusted server and malicious clients, guarantees the anonymity and unlinkability of accesses to such data, and allows the data owner to share outsourced data with other clients, selectively granting them read and write permissions. GORAM is the first system to achieve such a wide range of security and privacy properties for outsourced storage. In the process of designing an efficient construction, we developed two new, generally applicable cryptographic schemes, namely, batched zero-knowledge proofs of shuffle and an accountability technique based on chameleon signatures, which we consider of independent interest. We implemented GORAM in Amazon Elastic Compute Cloud (EC2) and ran a performance evaluation demonstrating the scalability and efficiency of our construction.